@article{Azencott2013,
abstract = {MOTIVATION: As an increasing number of genome-wide association studies reveal the limitations of the attempt to explain phenotypic heritability by single genetic loci, there is a recent focus on associating complex phenotypes with sets of genetic loci. Although several methods for multi-locus mapping have been proposed, it is often unclear how to relate the detected loci to the growing knowledge about gene pathways and networks. The few methods that take biological pathways or networks into account are either restricted to investigating a limited number of predetermined sets of loci or do not scale to genome-wide settings.$\backslash$n$\backslash$nRESULTS: We present SConES, a new efficient method to discover sets of genetic loci that are maximally associated with a phenotype while being connected in an underlying network. Our approach is based on a minimum cut reformulation of the problem of selecting features under sparsity and connectivity constraints, which can be solved exactly and rapidly. SConES outperforms state-of-the-art competitors in terms of runtime, scales to hundreds of thousands of genetic loci and exhibits higher power in detecting causal SNPs in simulation studies than other methods. On flowering time phenotypes and genotypes from Arabidopsis thaliana, SConES detects loci that enable accurate phenotype prediction and that are supported by the literature.$\backslash$n$\backslash$nAVAILABILITY: Code is available at http://webdav.tuebingen.mpg.de/u/karsten/Forschung/scones/.$\backslash$n$\backslash$nSUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.},
archivePrefix = {arXiv},
arxivId = {1211.2315},
author = {Azencott, Chlo{\'{e}} Agathe and Grimm, Dominik and Sugiyama, Mahito and Kawahara, Yoshinobu and Borgwardt, Karsten M.},
doi = {10.1093/bioinformatics/btt238},
eprint = {1211.2315},
file = {:Users/hclimente/Documents/Mendeley/Azencott et al/Bioinformatics/2013{\_}Efficient network-guided multi-locus association mapping with graph cuts.pdf:pdf},
isbn = {1367-4811 (Electronic)$\backslash$r1367-4803 (Linking)},
issn = {13674803},
journal = {Bioinformatics},
mendeley-groups = {GWAS/basics,GWAS/networks},
number = {13},
pages = {171--179},
pmid = {23812981},
title = {{Efficient network-guided multi-locus association mapping with graph cuts}},
volume = {29},
year = {2013}
}
@article{Yang2011,
abstract = {For most human complex diseases and traits, SNPs identified by genome-wide association studies (GWAS) explain only a small fraction of the heritability. Here we report a user-friendly software tool called genome-wide complex trait analysis (GCTA), which was developed based on a method we recently developed to address the "missing heritability" problem. GCTA estimates the variance explained by all the SNPs on a chromosome or on the whole genome for a complex trait rather than testing the association of any particular SNP to the trait. We introduce GCTA's five main functions: data management, estimation of the genetic relationships from SNPs, mixed linear model analysis of variance explained by the SNPs, estimation of the linkage disequilibrium structure, and GWAS simulation. We focus on the function of estimating the variance explained by all the SNPs on the X chromosome and testing the hypotheses of dosage compensation. The GCTA software is a versatile tool to estimate and partition complex trait variation with large GWAS data sets. ?? 2011 The American Society of Human Genetics.},
author = {Yang, Jian and Lee, S. Hong and Goddard, Michael E. and Visscher, Peter M.},
doi = {10.1016/j.ajhg.2010.11.011},
file = {:Users/hclimente/Documents/Mendeley/Yang et al/American Journal of Human Genetics/2011{\_}GCTA A tool for genome-wide complex trait analysis.pdf:pdf},
isbn = {1537-6605},
issn = {00029297},
journal = {American Journal of Human Genetics},
mendeley-groups = {GWAS/tools},
number = {1},
pages = {76--82},
pmid = {21167468},
publisher = {The American Society of Human Genetics},
title = {{GCTA: A tool for genome-wide complex trait analysis}},
url = {http://dx.doi.org/10.1016/j.ajhg.2010.11.011},
volume = {88},
year = {2011}
}
